calculate errors

aaa_helpers.txt

@@ -1,7 +1,18 @@
 nohup python cvae.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/review/abl_all_10p_curv_10_r2.out 2>&1 &
 nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_10_10p_r2" > nohup/review/curv_1_orig_curv_10_10p_r2.out 2>&1 &
 nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_25_10p_r2" > nohup/review/curv_1_orig_curv_25_10p_r2.out 2>&1 &
-nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_100_10p_r2" > nohup/review/curv_1_orig_curv_100_10p_r2.out 2>&1 &
+nohup python auto_encoder_curv.py --name="lhunor_ae_curv_1_orig_curv_100_10p_r2_2" > nohup/review/curv_1_orig_curv_100_10p_r2_2.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/review/abl_all_10p_curv_10_r2.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_all_10p_curv_25_r2" > nohup/review/abl_all_10p_curv_25_r2.out 2>&1 &
+nohup python cvae.py --name="lhunor_abl_all_10p_curv_100_r2" > nohup/review/abl_all_10p_curv_100_r2.out 2>&1 &
+nohup python evaluate_ae.py --name="lhunor_abl_all_10p_curv_100_r2" > nohup/evaluate/lhunor_abl_all_10p_curv_100_r2.out 2>&1 &
+nohup python evaluate_ae.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/evaluate/lhunor_abl_all_10p_curv_10_r2.out 2>&1 &
+nohup python evaluate_ae.py --name="lhunor_abl_all_10p_curv_25_r2" > nohup/evaluate/lhunor_abl_all_10p_curv_25_r2.out 2>&1 &
+nohup python calculate_error.py --name="lhunor_abl_all_10p_curv_100_r2" > nohup/error/abl_all_10p_curv_100_r2.out 2>&1 &
+nohup python calculate_error.py --name="lhunor_abl_all_10p_curv_10_r2" > nohup/error/abl_all_10p_curv_10_r2.out 2>&1 &
+nohup python calculate_error.py --name="lhunor_abl_all_10p_curv_25_r2" > nohup/error/abl_all_10p_curv_25_r2.out 2>&1 &
+
+
 
 one final convolutional layer for smoothing, on the final coordinates
 

auto_encoder_curv.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_ae_curv_post import config
+from configs.config_ae_curv_1_orig_curv_10_10p_r2_2 import config
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 
 import tensorflow as tf
@@ -31,13 +31,13 @@ import plotly.express as ex
 from tf_utils import check_tf_version, allow_memory_growth
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.custom_utils import batch_to_dict
-from models.cond_encoder import AutoEncoderNew as AutoEncoder
+from models.cond_encoder import AutoEncoder
 
 
 testing = False
 def init_wandb():
     if not testing:
-        run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp/')
+        run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/home/workspace/local-data/temp/', tags=config['tags'])
         artifact = wandb.Artifact('code', type='code')
         artifact.add_file('auto_encoder_curv.py')
         artifact.add_file('losses.py')
@@ -47,7 +47,9 @@ def init_wandb():
         artifact.add_file('convnext/models/convnext_tf.py')
         config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.')]
         for config_file_name in config_file_names:
-            artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
+            file_name = '/'.join(config_file_name.split('.')) + '.py'
+            if os.path.exists(file_name):
+                artifact.add_file(file_name)
         run.log_artifact(artifact)
 
 uv2mesh = UV2MeshLabel(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
@@ -96,46 +98,73 @@ def dice_dense(prediction, ground_truth, weight_map=None):
 
 
 @tf.function
-def train_step(batch):
-    with tf.GradientTape() as tape:
-        pred = model([batch['uv_curvature'], batch['uv_mask']], training=True)
+def train_step(batch):	
+    with tf.GradientTape() as tape:	
+        pred = model([batch['uv_curvature'], batch['uv_mask']], training=True)	
+        ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)	
+        ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))	
+        loss = ce_uv_loss	
+    pred_mesh = tf.math.argmax(uv2mesh(pred), axis=-1)	
+    mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))	
+    mean_iou_mesh = get_mean_iou(tf.cast(batch['mesh_curvature'], tf.float32) * batch['mesh_mask'], tf.cast(pred_mesh, tf.float32) * batch['mesh_mask'])	
+    dice = dice_dense(pred, batch['uv_curvature'])	
+    	
+    gradients = tape.gradient(loss, model.trainable_variables)	
+    optimizer.apply_gradients(zip(gradients, model.trainable_variables))	
+    return { 'train/loss': loss, 'train/ce_uv_loss': ce_uv_loss, 'train/ssim': ms_ssim, 'train/mean_iou': mean_iou, 'train/mean_iou_mesh': mean_iou_mesh, 'train/dice': dice }	
+        	
+@tf.function	
+def valid_step(batch):	
+    pred = model([batch['uv_curvature'], batch['uv_mask']], training=False)	
+    ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)	
+    ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))	
+    loss = ce_uv_loss	
+    pred_mesh = tf.math.argmax(uv2mesh(pred), axis=-1)	
+    mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))	
+    mean_iou_mesh = get_mean_iou(tf.cast(batch['mesh_curvature'], tf.float32) * batch['mesh_mask'], tf.cast(pred_mesh, tf.float32) * batch['mesh_mask'])	
+    dice = dice_dense(pred, batch['uv_curvature'])	
+    return { 'valid/loss': loss, 'valid/ce_uv_loss': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mean_iou': mean_iou, 'valid/mean_iou_mesh': mean_iou_mesh, 'valid/dice': dice }
 
-        ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
-        ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
+# def train_step(batch):
+#     with tf.GradientTape() as tape:
+#         pred = model([batch['uv_curvature'], batch['uv_mask']], training=True)
 
-        mesh_pred = uv2mesh(pred) * batch['mesh_mask'][:,:,tf.newaxis]
-        mesh_orig = tf.cast(uv2mesh(tf.math.argmax(batch['uv_curvature'], axis=-1, output_type=tf.int32)), tf.float32) * batch['mesh_mask']
-        ce_mesh_loss = scce(mesh_orig, mesh_pred)
+#         ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
+#         ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
 
-        loss = ce_uv_loss + ce_mesh_loss
+#         mesh_pred = uv2mesh(pred) * batch['mesh_mask'][:,:,tf.newaxis]
+#         mesh_orig = tf.cast(uv2mesh(tf.math.argmax(batch['uv_curvature'], axis=-1, output_type=tf.int32)), tf.float32) * batch['mesh_mask']
+#         ce_mesh_loss = scce(mesh_orig, mesh_pred)
 
-    mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
-    dice = dice_dense(pred, batch['uv_curvature'])
+#         loss = ce_uv_loss + ce_mesh_loss
+
+#     mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
+#     dice = dice_dense(pred, batch['uv_curvature'])
     
-    gradients = tape.gradient(loss, model.trainable_variables)
-    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
-    return { 'train/loss': loss, 'train/ce_uv_loss': ce_uv_loss, 'train/ssim': ms_ssim, 'train/mean_iou': mean_iou,
-             'train/dice': dice, 'train/ce_mesh_loss': ce_mesh_loss }
+#     gradients = tape.gradient(loss, model.trainable_variables)
+#     optimizer.apply_gradients(zip(gradients, model.trainable_variables))
+#     return { 'train/loss': loss, 'train/ce_uv_loss': ce_uv_loss, 'train/ssim': ms_ssim, 'train/mean_iou': mean_iou,
+#              'train/dice': dice, 'train/ce_mesh_loss': ce_mesh_loss }
         
 
 
-@tf.function
-def valid_step(batch):
-    pred = model([batch['uv_curvature'], batch['uv_mask']], training=False)
+# @tf.function
+# def valid_step(batch):
+#     pred = model([batch['uv_curvature'], batch['uv_mask']], training=False)
 
-    ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
-    ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
+#     ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
+#     ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
 
-    mesh_pred = uv2mesh(pred) * batch['mesh_mask'][:,:,tf.newaxis]
-    mesh_orig = tf.cast(uv2mesh(tf.math.argmax(batch['uv_curvature'], axis=-1, output_type=tf.int32)), tf.float32) * batch['mesh_mask']
-    ce_mesh_loss = scce(mesh_orig, mesh_pred)
-    loss = ce_uv_loss + ce_mesh_loss
+#     mesh_pred = uv2mesh(pred) * batch['mesh_mask'][:,:,tf.newaxis]
+#     mesh_orig = tf.cast(uv2mesh(tf.math.argmax(batch['uv_curvature'], axis=-1, output_type=tf.int32)), tf.float32) * batch['mesh_mask']
+#     ce_mesh_loss = scce(mesh_orig, mesh_pred)
+#     loss = ce_uv_loss + ce_mesh_loss
 
-    mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
-    dice = dice_dense(pred, batch['uv_curvature'])
+#     mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
+#     dice = dice_dense(pred, batch['uv_curvature'])
 
-    return { 'valid/loss': loss, 'valid/ce_uv_loss': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mean_iou': mean_iou,
-             'valid/dice': dice, 'valid/ce_mesh_loss': ce_mesh_loss }
+#     return { 'valid/loss': loss, 'valid/ce_uv_loss': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mean_iou': mean_iou,
+#              'valid/dice': dice, 'valid/ce_mesh_loss': ce_mesh_loss }
         
 
 def plot_everything(batch: dict, type: str):
@@ -206,7 +235,7 @@ def test():
 
 if __name__ == '__main__':
     init_wandb()
-    allow_memory_growth()
+    # allow_memory_growth()
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str)
     args = vars(parser.parse_args())
@@ -226,7 +255,8 @@ if __name__ == '__main__':
                                         in_memory=config['dataset_in_memory'],
                                         use_no_orient=config['remove_global_orientation'],
                                         frames_file=config['frames_file'] if 'frames_file' in config.keys() else None, 
-                                        is_cape=config['is_cape'])
+                                        is_cape=config['is_cape'],
+                                        curv_neighbourhood=config['curv_neighbourhood'])
     static_dataset_train = StaticDataset(folder=config['train_dir'],
                                          signature=config['signature_static'], 
                                          resolutions=[config['resolution']], 
@@ -240,7 +270,8 @@ if __name__ == '__main__':
                                         in_memory=config['dataset_in_memory'], 
                                         use_no_orient=config['remove_global_orientation'], 
                                         frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None, 
-                                        is_cape=config['is_cape'])
+                                        is_cape=config['is_cape'],
+                                        curv_neighbourhood=config['curv_neighbourhood'])
     static_dataset_valid = StaticDataset(folder=config['valid_dir'],
                                          signature=config['signature_static'], 
                                          resolutions=[config['resolution']], 

calculate_error.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_all_post import config
+from configs.config_abl_all_10p_curv_25_r2 import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 # import tensorflow as tf
@@ -59,7 +59,7 @@ if __name__ == '__main__':
     folder = config['test_dir']
 
     checkpoint_name = config['name']
-    iteration = ''
+    iteration = '70000'
 
     F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
     garment_type_table = np.load('custom_data/garment_type_table_test.npy', allow_pickle=True).item()

configs/config_abl_all_10p_curv_100_r2.py

@@ -0,0 +1,125 @@
+config = dict()
+
+# DON'T FORGET TO RESET SSIM LOSS USAGE
+# DON'T FORGET TO CHECK MODEL LOADING
+# DON'T FORGET TO CHECK IF STORE IN MEMORY
+# DON'T FORGET TO CHECK REG LOSS COEFF
+# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
+# DON'T FORGET TO CHECK AVG GARMENT USED
+
+config['use_skinning'] =              True # and deshaping
+config['remove_global_orientation'] = True
+config['use_curvature'] =             True
+config['use_extra_losses'] =          True # edge, collision, 3d mesh
+config['use_offsets'] =               True
+
+config['use_norm_loss'] =             True
+config['only_curvatures'] =           False
+
+config['curv_neighbourhood'] = 100
+
+ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
+assert ablation_count == 1 or ablation_count == 0
+
+config['gpu'] = '1'
+config['name'] = 'abl_all_10p_curv_100_r2'
+config['notes'] = ''
+config['tags'] = ['review_2']
+
+config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv  train_no_shape_one_hot_avg_uv
+if config['use_skinning']:
+  assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
+
+config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
+
+config['batch_size'] = 4
+config['resolution'] = 512
+config['is_skirt'] = True
+config['dataset_in_memory'] = False
+config['is_cape'] = False
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+# config['frames_file_test'] = 'frames_dress_30k.txt'
+
+config['static_model'] = 'checkpoints/static_1_skirt'
+config['body_model'] = 'checkpoints/body_1_skirt_2_2'
+config['curvature_model'] = 'checkpoints/post/curv_1_orig_curv_100_10p_r2_2'
+
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+
+config['pretrained_model'] = ''
+config['latent_dim'] = 128
+
+###############    
+config['lr'] = 0.0001#                 0.0001
+###############
+config['starting_balancing_coeff'] = 0.001
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'pose_shape': (72 + 11,),
+                          'uv_body_posed': (512, 256, 3),
+                          'mesh_body_posed': (config['smpl_res'], 3,),
+                          'mesh_posed': (config['smpl_res'], 3,),
+                          'uv_curvature': (512, 256, 7),
+                        }
+config['signature_static'] = { 'uv_static': (512, 256, 3),
+                              'mesh_mask': (config['smpl_res'],),
+                              'uv_mask': (512, 256,),
+                              }
+
+if config['use_skinning']:
+  config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
+  config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
+else:
+  config['signature_dynamic']['uv_posed'] = (512, 256, 3)
+
+
+config['upscale_enhancer'] = ''
+config['freeze_encoders'] = True
+config['use_one_hot'] = False
+
+config['cvae'] = dict()
+config['cvae']['is_label'] = False
+config['cvae']['latent_dim'] = config['latent_dim']
+config['cvae']['add_noise'] = True
+config['cvae']['noise_amount'] = 0.025
+
+config['cvae']['kernel_size'] = 7
+config['cvae']['encoder_depths'] = [3, 3, 9, 3]
+config['cvae']['encoder_dims'] = [32, 64, 128, 256]
+config['cvae']['decoder_input'] = (16, 8, 80)
+config['cvae']['decoder_depths'] = [3, 3, 9, 3]
+config['cvae']['decoder_dims'] = [256, 128, 64, 16]
+
+
+config['cond_ae'] = dict()
+config['cond_ae']['is_label'] = False
+config['cond_ae']['upscale_output'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['bottleneck_size'] = 64
+config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
+config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
+config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
+config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
+config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
+
+config['cond_ae_body'] = dict()
+config['cond_ae_body']['latent_dim'] = config['latent_dim']
+config['cond_ae_body']['kernel_size'] = 7
+config['cond_ae_body']['upscale_output'] = True
+config['cond_ae_body']['bottleneck_size'] = 512
+config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
+config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
+config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
+config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
+config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]

configs/config_abl_all_10p_curv_10_r2.py

@@ -0,0 +1,125 @@
+config = dict()
+
+# DON'T FORGET TO RESET SSIM LOSS USAGE
+# DON'T FORGET TO CHECK MODEL LOADING
+# DON'T FORGET TO CHECK IF STORE IN MEMORY
+# DON'T FORGET TO CHECK REG LOSS COEFF
+# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
+# DON'T FORGET TO CHECK AVG GARMENT USED
+
+config['use_skinning'] =              True # and deshaping
+config['remove_global_orientation'] = True
+config['use_curvature'] =             True
+config['use_extra_losses'] =          True # edge, collision, 3d mesh
+config['use_offsets'] =               True
+
+config['use_norm_loss'] =             True
+config['only_curvatures'] =           False
+
+config['curv_neighbourhood'] = 10
+
+ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
+assert ablation_count == 1 or ablation_count == 0
+
+config['gpu'] = '6'
+config['name'] = 'abl_all_10p_curv_10_r2_2'
+config['notes'] = ''
+config['tags'] = ['review_2']
+
+config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv  train_no_shape_one_hot_avg_uv
+if config['use_skinning']:
+  assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
+
+config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
+
+config['batch_size'] = 4
+config['resolution'] = 512
+config['is_skirt'] = True
+config['dataset_in_memory'] = True
+config['is_cape'] = False
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+# config['frames_file_test'] = 'frames_dress_30k.txt'
+
+config['static_model'] = 'checkpoints/static_1_skirt'
+config['body_model'] = 'checkpoints/body_1_skirt_2_2'
+config['curvature_model'] = 'checkpoints/post/curv_1_orig_curv_10_10p_r2_2'
+
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+
+config['pretrained_model'] = ''
+config['latent_dim'] = 128
+
+###############    
+config['lr'] = 0.0001#                 0.0001
+###############
+config['starting_balancing_coeff'] = 0.001
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'pose_shape': (72 + 11,),
+                          'uv_body_posed': (512, 256, 3),
+                          'mesh_body_posed': (config['smpl_res'], 3,),
+                          'mesh_posed': (config['smpl_res'], 3,),
+                          'uv_curvature': (512, 256, 7),
+                        }
+config['signature_static'] = { 'uv_static': (512, 256, 3),
+                              'mesh_mask': (config['smpl_res'],),
+                              'uv_mask': (512, 256,),
+                              }
+
+if config['use_skinning']:
+  config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
+  config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
+else:
+  config['signature_dynamic']['uv_posed'] = (512, 256, 3)
+
+
+config['upscale_enhancer'] = ''
+config['freeze_encoders'] = True
+config['use_one_hot'] = False
+
+config['cvae'] = dict()
+config['cvae']['is_label'] = False
+config['cvae']['latent_dim'] = config['latent_dim']
+config['cvae']['add_noise'] = True
+config['cvae']['noise_amount'] = 0.025
+
+config['cvae']['kernel_size'] = 7
+config['cvae']['encoder_depths'] = [3, 3, 9, 3]
+config['cvae']['encoder_dims'] = [32, 64, 128, 256]
+config['cvae']['decoder_input'] = (16, 8, 80)
+config['cvae']['decoder_depths'] = [3, 3, 9, 3]
+config['cvae']['decoder_dims'] = [256, 128, 64, 16]
+
+
+config['cond_ae'] = dict()
+config['cond_ae']['is_label'] = False
+config['cond_ae']['upscale_output'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['bottleneck_size'] = 64
+config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
+config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
+config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
+config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
+config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
+
+config['cond_ae_body'] = dict()
+config['cond_ae_body']['latent_dim'] = config['latent_dim']
+config['cond_ae_body']['kernel_size'] = 7
+config['cond_ae_body']['upscale_output'] = True
+config['cond_ae_body']['bottleneck_size'] = 512
+config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
+config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
+config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
+config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
+config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]

configs/config_abl_all_10p_curv_25_r2.py

@@ -0,0 +1,125 @@
+config = dict()
+
+# DON'T FORGET TO RESET SSIM LOSS USAGE
+# DON'T FORGET TO CHECK MODEL LOADING
+# DON'T FORGET TO CHECK IF STORE IN MEMORY
+# DON'T FORGET TO CHECK REG LOSS COEFF
+# DON'T FORGET TO LOAD FINAL UPSCALER UNLESS HAVE LEVEL 512
+# DON'T FORGET TO CHECK AVG GARMENT USED
+
+config['use_skinning'] =              True # and deshaping
+config['remove_global_orientation'] = True
+config['use_curvature'] =             True
+config['use_extra_losses'] =          True # edge, collision, 3d mesh
+config['use_offsets'] =               True
+
+config['use_norm_loss'] =             True
+config['only_curvatures'] =           False
+
+config['curv_neighbourhood'] = 25
+
+ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
+assert ablation_count == 1 or ablation_count == 0
+
+config['gpu'] = '0'
+config['name'] = 'abl_all_10p_curv_25_r2'
+config['notes'] = ''
+config['tags'] = ['review_2']
+
+config['avg_garment_path'] = 'custom_data/train_no_shape_skirt_avg_uv.npy' #custom_data/train_no_shape_avg_uv.npy # train_no_shape_avg_uv  train_no_shape_one_hot_avg_uv
+if config['use_skinning']:
+  assert (config['use_offsets'] and config['avg_garment_path'] != '') or (not config['use_offsets'] and config['avg_garment_path'] == '')
+
+config['reg_loss_coeff'] = 0.1 # set to 0 if don't want to use edge+collision
+
+config['batch_size'] = 4
+config['resolution'] = 512
+config['is_skirt'] = True
+config['dataset_in_memory'] = True
+config['is_cape'] = False
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+# config['frames_file_test'] = 'frames_dress_30k.txt'
+
+config['static_model'] = 'checkpoints/static_1_skirt'
+config['body_model'] = 'checkpoints/body_1_skirt_2_2'
+config['curvature_model'] = 'checkpoints/post/curv_1_orig_curv_25_10p_r2'
+
+
+config['smpl_res'] = 14475
+if config['is_cape']:
+  config['smpl_res'] = 3638
+
+
+config['pretrained_model'] = ''
+config['latent_dim'] = 128
+
+###############    
+config['lr'] = 0.0001#                 0.0001
+###############
+config['starting_balancing_coeff'] = 0.001
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'pose_shape': (72 + 11,),
+                          'uv_body_posed': (512, 256, 3),
+                          'mesh_body_posed': (config['smpl_res'], 3,),
+                          'mesh_posed': (config['smpl_res'], 3,),
+                          'uv_curvature': (512, 256, 7),
+                        }
+config['signature_static'] = { 'uv_static': (512, 256, 3),
+                              'mesh_mask': (config['smpl_res'],),
+                              'uv_mask': (512, 256,),
+                              }
+
+if config['use_skinning']:
+  config['signature_dynamic']['uv_unposed'] = (512, 256, 3)
+  config['signature_dynamic']['mesh_unposed'] = (config['smpl_res'], 3,)
+else:
+  config['signature_dynamic']['uv_posed'] = (512, 256, 3)
+
+
+config['upscale_enhancer'] = ''
+config['freeze_encoders'] = True
+config['use_one_hot'] = False
+
+config['cvae'] = dict()
+config['cvae']['is_label'] = False
+config['cvae']['latent_dim'] = config['latent_dim']
+config['cvae']['add_noise'] = True
+config['cvae']['noise_amount'] = 0.025
+
+config['cvae']['kernel_size'] = 7
+config['cvae']['encoder_depths'] = [3, 3, 9, 3]
+config['cvae']['encoder_dims'] = [32, 64, 128, 256]
+config['cvae']['decoder_input'] = (16, 8, 80)
+config['cvae']['decoder_depths'] = [3, 3, 9, 3]
+config['cvae']['decoder_dims'] = [256, 128, 64, 16]
+
+
+config['cond_ae'] = dict()
+config['cond_ae']['is_label'] = False
+config['cond_ae']['upscale_output'] = False
+config['cond_ae']['latent_dim'] = config['latent_dim']
+config['cond_ae']['kernel_size'] = 7
+config['cond_ae']['bottleneck_size'] = 64
+config['cond_ae']['encoder_depths'] = [3, 3, 9, 9, 3]
+config['cond_ae']['encoder_dims'] = [32, 64, 128, 256, config['cond_ae']['bottleneck_size']]
+config['cond_ae']['decoder_input'] = (8, 4, config['cond_ae']['bottleneck_size'])
+config['cond_ae']['decoder_depths'] = [3, 3, 9, 3, 3]
+config['cond_ae']['decoder_dims'] = [config['cond_ae']['bottleneck_size'], 256, 128, 64, 16]
+
+config['cond_ae_body'] = dict()
+config['cond_ae_body']['latent_dim'] = config['latent_dim']
+config['cond_ae_body']['kernel_size'] = 7
+config['cond_ae_body']['upscale_output'] = True
+config['cond_ae_body']['bottleneck_size'] = 512
+config['cond_ae_body']['encoder_depths'] = [3, 3, 9, 9, 3, 3]
+config['cond_ae_body']['encoder_dims'] = [32, 64, 128, 256, 256, config['cond_ae_body']['bottleneck_size']]
+config['cond_ae_body']['decoder_input'] = (4, 2, config['cond_ae_body']['bottleneck_size'])
+config['cond_ae_body']['decoder_depths'] = [3, 3, 9, 3]
+config['cond_ae_body']['decoder_dims'] = [config['cond_ae_body']['bottleneck_size'], 128, 64, 16]

configs/config_ae_curv.py

@@ -1,13 +1,23 @@
 from configs.config_base import config
 
-config['gpu'] = '5'
+config['gpu'] = '4'
 config['lr'] = 0.001
-config['name'] = 'curv_1' #'ae_curv_9'
+config['name'] = 'curv_1_orig_r2' #'ae_curv_9'
 config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
 | \
   '
+config['tags'] = ['review_2']
+config['remove_global_orientation'] = True
 
 config['cond_ae']['is_label'] = True
+config['is_skirt'] = True
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+config['curv_neighbourhood'] = 10
 
 config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
                           'uv_curvature': (512, 256, 7),

configs/config_ae_curv_1_orig_curv_100_10p_r2.py

@@ -0,0 +1,30 @@
+from configs.config_base import config
+
+config['gpu'] = '1'
+config['lr'] = 0.001
+config['name'] = 'curv_1_orig_curv_100_10p_r2' #'ae_curv_9'
+config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
+| \
+  '
+config['tags'] = ['review_2']
+config['remove_global_orientation'] = True
+config['dataset_in_memory'] = True
+
+config['cond_ae']['is_label'] = True
+config['is_skirt'] = True
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+config['curv_neighbourhood'] = 100
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'uv_curvature': (512, 256, 7),
+                          'mesh_curvature':  (config['smpl_res'])
+                        }
+config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
+                         'uv_mask': (512, 256,),
+                        }
+

configs/config_ae_curv_1_orig_curv_100_10p_r2_2.py

@@ -0,0 +1,30 @@
+from configs.config_base import config
+
+config['gpu'] = '1'
+config['lr'] = 0.001
+config['name'] = 'curv_1_orig_curv_100_10p_r2_2' #'ae_curv_9'
+config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
+| \
+  '
+config['tags'] = ['review_2']
+config['remove_global_orientation'] = True
+config['dataset_in_memory'] = True
+
+config['cond_ae']['is_label'] = True
+config['is_skirt'] = True
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+config['curv_neighbourhood'] = 100
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'uv_curvature': (512, 256, 7),
+                          'mesh_curvature':  (config['smpl_res'])
+                        }
+config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
+                         'uv_mask': (512, 256,),
+                        }
+

configs/config_ae_curv_1_orig_curv_10_10p_r2.py

@@ -0,0 +1,30 @@
+from configs.config_base import config
+
+config['gpu'] = '4'
+config['lr'] = 0.001
+config['name'] = 'curv_1_orig_curv_10_10p_r2' #'ae_curv_9'
+config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
+| \
+  '
+config['tags'] = ['review_2']
+config['remove_global_orientation'] = True
+config['dataset_in_memory'] = True
+
+config['cond_ae']['is_label'] = True
+config['is_skirt'] = True
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+config['curv_neighbourhood'] = 10
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'uv_curvature': (512, 256, 7),
+                          'mesh_curvature':  (config['smpl_res'])
+                        }
+config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
+                         'uv_mask': (512, 256,),
+                        }
+

configs/config_ae_curv_1_orig_curv_10_10p_r2_2.py

@@ -0,0 +1,30 @@
+from configs.config_base import config
+
+config['gpu'] = '4'
+config['lr'] = 0.001
+config['name'] = 'curv_1_orig_curv_10_10p_r2_2' #'ae_curv_9'
+config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
+| \
+  '
+config['tags'] = ['review_2']
+config['remove_global_orientation'] = True
+config['dataset_in_memory'] = True
+
+config['cond_ae']['is_label'] = True
+config['is_skirt'] = True
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+config['curv_neighbourhood'] = 10
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'uv_curvature': (512, 256, 7),
+                          'mesh_curvature':  (config['smpl_res'])
+                        }
+config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
+                         'uv_mask': (512, 256,),
+                        }
+

configs/config_ae_curv_1_orig_curv_25_10p_r2.py

@@ -0,0 +1,30 @@
+from configs.config_base import config
+
+config['gpu'] = '6'
+config['lr'] = 0.001
+config['name'] = 'curv_1_orig_curv_25_10p_r2' #'ae_curv_9'
+config['notes'] = 'convnext + convnext, 16*8*32, ce loss. flattened latent dim of 128. trained on 100 garments. switched back to sparse CCE, normal one was giving NaN | \
+| \
+  '
+config['tags'] = ['review_2']
+config['remove_global_orientation'] = True
+config['dataset_in_memory'] = True
+
+config['cond_ae']['is_label'] = True
+config['is_skirt'] = True
+
+config['train_dir'] = '/home/workspace/data/dataset/local-cache/train_skirt'
+config['valid_dir'] = '/home/workspace/local-data/valid_skirt'
+config['test_dir'] = '/home/workspace/data/dataset/test_skirt_ready_2'
+config['frames_file'] = 'frames_skirt_10p.txt'
+config['frames_file_valid'] = 'frames_skirt_10p.txt'
+config['curv_neighbourhood'] = 25
+
+config['signature_dynamic'] = { 'id': (), 'frame_nr': (),
+                          'uv_curvature': (512, 256, 7),
+                          'mesh_curvature':  (config['smpl_res'])
+                        }
+config['signature_static'] = { 'mesh_mask': (config['smpl_res'],),
+                         'uv_mask': (512, 256,),
+                        }
+

convert_to_uv_local_single.py

@@ -384,9 +384,9 @@ def test(verts, faces, values):
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument("-f", "--folder", help = "end of range", default='/home/workspace/data/dataset/test_skirt_ready_2')
+    parser.add_argument("-f", "--folder", help = "end of range", default='/home/workspace/local-data/valid')
     parser.add_argument("-s", "--start", help = "start of range", default=0)
-    parser.add_argument("-e", "--end", help = "end of range", default=30000)
+    parser.add_argument("-e", "--end", help = "end of range", default=1500)
     parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
     parser.add_argument("-sk", "--skirt", help = "end of range", default=1)
     args = parser.parse_args()
@@ -408,7 +408,7 @@ if __name__ == '__main__':
     m2uv = np.load('custom_utils/m2uv' + ("_skirt" if args.skirt == 1 else "") + '.npy', allow_pickle=True).item()
     m2uv = m2uv_array(m2uv)
     uv_mask = np.load('custom_utils/mask_256x512' + ("_skirt" if args.skirt == 1 else "") + '.npy')
-    curvature_label_type = '6_25 '
+    curvature_label_type = '6_25'
 
     uv_res = 256
     uv_mask = (cv2.resize(uv_mask * 255.0, (uv_res, uv_res * 2)) / 255).astype(bool)[:, :, np.newaxis]
@@ -436,23 +436,6 @@ if __name__ == '__main__':
 
     # ids = os.listdir(os.path.join(local_folder, 'mesh_static'))[int(args.start):int(args.end)]
     # ids = [id.split('.')[0] for id in ids]
-
-    is_skirt = True if args.skirt == 1 else False
-    skirt_modifier = '_skirt' if is_skirt else ''
-
-    folder = args.folder
-    frames_file = 'frames' + skirt_modifier + '_10p.txt'
-    neighbourhood = 10
-
-    # frames = []
-    # with open(os.path.join(folder, frames_file), 'r') as f:
-    #     lines = f.readlines()
-    #     lines = [line.strip() for line in lines]
-    # frames = lines#[int(args.start):int(args.end)]
-    # ids = [frame.split('_')[0] for frame in frames]
-
-    print(f'params: -f {args.folder} -s {args.start} -e {args.end} -n {args.name} -skirt {args.skirt} -neigh {neighbourhood}')
-
     ids = os.listdir(os.path.join(local_folder, 'pose_shape'))
     ids = [id.split('_')[0] for id in ids]
     ids = sorted(list(set(ids)))[int(args.start):int(args.end)]
@@ -469,16 +452,16 @@ if __name__ == '__main__':
         # plt.show()
         # pass
 
-        frames_len = glob.glob(os.path.join(local_folder, 'pose_shape', id + '_*'))
-        frames_len = len(frames_len)
-        assert frames_len <= 300
+        frames = glob.glob(os.path.join(local_folder, 'pose_shape', id + '_*'))
+        frames = len(frames)
+        assert frames <= 300
 
         # offsets = sio.loadmat(os.path.join(folder, id, garment + '_offsets_static.mat'))['label'][:,:3]
         verts_static = np.load(os.path.join(local_folder, 'mesh_static', id + '.npz'))['mesh_static']
         mask = np.load(os.path.join(local_folder, 'mesh_mask', id + '.npy')).astype(bool)
         # body = readPC2(os.path.join(folder, id, 'Body_posed.pc16'),  True)['V']
         # gt = readPC2(os.path.join(folder, id, garment + '_registered.pc16'),  True)['V']
-        curvature = np.stack([np.load(os.path.join(local_folder, f'mesh_curvature_{neighbourhood}_2',  f'{id}_{i}.npz'))['mesh_curvature'] for i in range(frames_len)])
+        # curvature = np.load(os.path.join(folder, id, 'curvature_label_' + curvature_label_type + '.npy'))
         # mesh_unposed = [np.load(os.path.join(folder, id, 'mesh_unposed',  id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
         # mesh_unposed = np.stack([mesh_unposed[i]['mesh_unposed'] for i in range(gt.shape[0])])
         # mesh_unposed_no_shape = [np.load(os.path.join(folder, id, 'mesh_unposed_no_shape',  id + '_' + str(i) + '.npz')) for i in range(gt.shape[0])]
@@ -487,8 +470,8 @@ if __name__ == '__main__':
         # body_no_orient = np.stack([body_no_orient[i]['mesh_posed_body_no_orient'] for i in range(gt.shape[0])])
         # mesh_unposed_no_shape_one_hot = [np.load(os.path.join(local_folder, 'mesh_unposed_no_shape_one_hot',  id + '_' + str(i) + '.npz')) for i in range(frames)]
         # mesh_unposed_no_shape_one_hot = [mesh_unposed_no_shape_one_hot[i]['mesh_unposed_no_shape'] for i in range(frames)]
-        # mesh_posed_no_orient = [np.load(os.path.join(local_folder, 'mesh_posed_no_orient',  id + '_' + str(i) + '.npz')) for i in range(frames)]
-        # mesh_posed_no_orient = [mesh_posed_no_orient[i]['mesh_posed_no_orient'] for i in range(frames)]
+        mesh_posed_no_orient = [np.load(os.path.join(local_folder, 'mesh_posed_no_orient',  id + '_' + str(i) + '.npz')) for i in range(frames)]
+        mesh_posed_no_orient = [mesh_posed_no_orient[i]['mesh_posed_no_orient'] for i in range(frames)]
 
 
         # verts_dynamic = gt
@@ -508,11 +491,11 @@ if __name__ == '__main__':
         # uv_body = convert_to_uv_batch(uv_res, m2uv, uv_map, body[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
         # uv_posed_body_no_orient = convert_to_uv_batch(uv_res, m2uv, uv_map, body_no_orient[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
         # uv_gt = convert_to_uv_batch(uv_res, m2uv, uv_map, gt[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
-        uv_curvature = convert_to_uv_batch_label(uv_res, m2uv, uv_map, curvature[:, :], neighbours=3, mask=mask, method='nearest', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
+        # uv_curvature = convert_to_uv_batch_label(uv_res, m2uv, uv_map, curvature[:, :], neighbours=3, mask=mask, method='nearest', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
         # uv_unposed = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
         # uv_unposed_no_shape = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed_no_shape[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
         # uv_unposed_no_shape_one_hot = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed_no_shape_one_hot, neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border, id=id)
-        # uv_posed_no_orient = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_posed_no_orient, neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border, id=id)
+        uv_posed_no_orient = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_posed_no_orient, neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border, id=id)
         # uv_shaped_body = convert_to_uv(uv_res, m2uv, uv_map, shaped_body, neighbours=3, mask=None, method='rbf', garment_uv_mask=None).astype(np.float32)
         print(time.perf_counter() - start)
 
@@ -533,9 +516,9 @@ if __name__ == '__main__':
         # for i in range(uv_gt.shape[0]):
         #     np.savez_compressed(os.path.join(folder, id, 'gt_uv', garment + '_' + str(i) + '.npz'), uv_gt=uv_gt[i, :, :, :])
 
-        os.makedirs(os.path.join(folder, f'uv_curvature_{neighbourhood}_2'), exist_ok=True)
-        for i in range(uv_curvature.shape[0]):
-            np.savez_compressed(os.path.join(folder, f'uv_curvature_{neighbourhood}_2', id + '_' + str(i) + '.npz'), curvature_uv=uv_curvature[i, :, :])
+        # os.makedirs(os.path.join(folder, id, 'curvature_uv'), exist_ok=True)
+        # for i in range(uv_curvature.shape[0]):
+        #     np.savez_compressed(os.path.join(folder, id, 'curvature_uv', garment + '_' + str(i) + '.npz'), curvature_uv=uv_curvature[i, :, :])
 
         # os.makedirs(os.path.join(folder, id, 'uv_unposed'), exist_ok=True)
         # for i in range(uv_unposed.shape[0]):

convnext/models/convnext_tf.py

@@ -366,8 +366,8 @@ class ConvNeXtReverseLabels(tf.keras.Model):
             self.stages.append(stage)
             cur += depths[i]
 
-        # self.last = Dense(nr_of_classes, activation='softmax')
-        self.last = Dense(nr_of_classes - 1)
+        self.last = Dense(nr_of_classes, activation='softmax')
+        # self.last = Dense(nr_of_classes - 1)
 
     def call(self, x, training):
         for i in reversed(range(len(self.downsample_layers))):

curvature_analysis.py

@@ -1,235 +0,0 @@
-import os
-import sys
-import tqdm
-import argparse
-import random
-import string
-import time
-import numpy as np
-import trimesh as tm
-import cmapy as cm
-import scipy as sp
-import scipy.io as sio
-import plotly.graph_objects as go
-from pyntcloud import PyntCloud
-from scipy.sparse import csr_matrix, coo_matrix
-from scipy.sparse.csgraph import floyd_warshall, dijkstra
-from plotly.subplots import make_subplots
-from custom_utils.custom_utils import _maskFaces
-
-from custom_utils.supersmpl_io import readPC2, readPC2Frame, writePC2, readOBJ
-from custom_utils.custom_utils import _maskFaces
-
-def export_to_ply(mesh, file):
-    ply = tm.exchange.ply.export_ply(mesh, encoding='binary', vertex_normal=True, include_attributes=True)
-    output_file = open(file, "wb+")
-    output_file.write(ply)
-    output_file.close()
-
-
-def get_closest_n_points(verts, idx, n):
-    dist = sp.spatial.distance.cdist(verts[idx, :], verts)
-    dist[dist == 0] = 1e9
-    idx_sort = np.argsort(dist, axis=-1)
-    closest = idx_sort[:, :n]
-    return closest
-
-
-def calculate_dist(folder, neighbourhood):
-    out = dict()
-    frames = sorted(os.listdir(os.path.join(folder, 'mesh_posed')))[int(args.start):int(args.end)]
-
-    for frame in tqdm.tqdm(frames):
-        id = frame.split('_')[0]
-        if id in out.keys():
-            continue
-
-        mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
-        k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
-        out[id] = k_neighbors
-
-    return out
-
-
-def histedges_equalN(x, nbin):
-    npt = len(x)
-    return np.interp(np.linspace(0, npt, nbin + 1),
-                     np.arange(npt),
-                     np.sort(x))
-
-
-def get_bins(values, nr_of_bins):
-    flattened = []
-    for c in values:
-        flattened.extend(c.flatten())
-    flattened = np.array(flattened)
-    bins = histedges_equalN(flattened, nr_of_bins)
-    return bins
-
-
-def generate_curvatures(folder, frame, bins, neighbourhoods=[25], nr_of_labels=[6]):
-    id = frame.split('_')[0]
-    frame_nr = int(frame.split('_')[1].split('.')[0])
-
-    mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
-    _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
-    v = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
-    
-    m = tm.Trimesh(v, faces, process=False, validate=False)
-
-    outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
-    outliers = np.nonzero(outliers)[0]
-    outliers_n_neighbours = get_closest_n_points(v, outliers, 2)
-    outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
-    faces = np.concatenate((faces, outliers_faces), axis=0)
-
-    verts = v
-
-    m = tm.Trimesh(verts, faces, process=False, validate=False)
-    export_to_ply(m, temp_file)
-
-    cloud = PyntCloud.from_file(temp_file)
-
-    curvatures = []
-    for neighbourhood, nr_labels in zip(neighbourhoods, nr_of_labels):
-        k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
-        #k_neighbors = k_neighbors_cache[id] # np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
-        ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
-        cloud.add_scalar_field("curvature", ev=ev)
-        curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
-        
-        bins_local = bins[neighbourhood] if bins is not None else get_bins(curvature, nr_labels)
-        labels = np.linspace(0, bins_local.shape[0] - 2, bins_local.shape[0] - 1)
-
-        frame_labels = np.zeros((curvature.shape[0]), dtype=np.float32)
-        for j in range(bins_local.shape[0] - 2):
-            frame_labels[(curvature >= bins_local[j]) * (curvature < bins_local[j+1])] = labels[j]
-        frame_labels[curvature >= bins_local[-2]] = labels[-1]
-
-        curvatures.append(frame_labels)
-        
-    return curvatures
-
-
-def visualize_curvature_labels(folder, frame, bins, neighbourhoods, nr_of_labels):
-    id = frame.split('_')[0]
-    frame_nr = frame.split('_')[1].split('.')[0]
-
-    mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
-    _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
-    verts = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
-    
-    curvature_labels_all = generate_curvatures(folder, frame, bins, neighbourhoods, nr_of_labels)
-    colors_binned_all = []
-
-    for curvature_labels in curvature_labels_all:
-        colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, int(curvature_labels.max()+1))])
-        colors_binned = np.zeros((curvature_labels.shape[0], 3))
-        for i in range(int(curvature_labels.max())+1):
-            # print(np.count_nonzero(curvature_labels == i))
-            colors_binned[curvature_labels == i, :] = colors[i]
-        colors_binned_all.append(colors_binned)
-
-
-    fig = make_subplots(rows=1, cols=len(colors_binned_all)+1, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(len(colors_binned_all)+1)] ])
-
-    colors = np.ones((verts.shape[0],3))*np.array([255,200,200])[None,:]
-
-    fig.add_trace(go.Mesh3d(
-                # 8 vertices of a cube
-                x=verts[:,0],
-                y=verts[:,1],
-                z=verts[:,2],
-                # i, j and k give the vertices of triangles
-                i = faces[:,0],
-                j = faces[:,1],
-                k = faces[:,2],
-                vertexcolor=colors,
-                lighting={ 'ambient':0.9, 'diffuse':0.5, 'roughness':0.5 }
-            ),
-            row=1, col=1)
-
-    for i, colors_binned in enumerate(colors_binned_all):
-        fig.add_trace(go.Mesh3d(
-                    # 8 vertices of a cube
-                    x=verts[:,0],
-                    y=verts[:,1],
-                    z=verts[:,2],
-                    # i, j and k give the vertices of triangles
-                    i = faces[:,0],
-                    j = faces[:,1],
-                    k = faces[:,2],
-                    vertexcolor=colors_binned,
-                    # lighting={ 'ambient':0.8, 'diffuse':0.5, 'roughness':0.5 }
-                ),
-                row=1, col=i+2)
-    
-
-
-    fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
-                    scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1),
-                    scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1),
-                    scene4_aspectmode='manual', scene4_aspectratio=dict(x=1, y=1, z=1),
-                    # scene5_aspectmode='manual', scene5_aspectratio=dict(x=1, y=1, z=1),
-                    )
-    helper = dict(xaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
-                yaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
-                zaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),)
-        
-    fig.update_layout(scene = helper,
-                        scene2 = helper,
-                        scene3 = helper,
-                        scene4 = helper,
-                        # scene5 = helper,
-                        )
-
-
-    # meshes = [go.Mesh3d(
-    #             x=x[:,0],
-    #             y=x[:,1],
-    #             z=x[:,2],
-    #             i = faces[:,0],
-    #             j = faces[:,1],
-    #             k = faces[:,2],
-    #             vertexcolor=colors_binned
-    #             )]
-
-    # fig = go.Figure(data=meshes)
-    # fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1))
-    # fig.update_layout(
-    #     scene = dict(
-    #         xaxis = dict(nticks=4, range=[-1,1],),
-    #                     yaxis = dict(nticks=4, range=[-1,1],),
-    #                     zaxis = dict(nticks=4, range=[-1,1],),)
-    #                     )
-
-    fig.show()
-
-    print()
-
-
-if __name__ == '__main__':
-
-    is_skirt = True
-    skirt_modifier = '_skirt' if is_skirt else ''
-    F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
-    # k_neighbours(k=25)
-
-    folder = '/home/workspace/local-data/valid_skirt'
-    bins = dict()
-    bins[10] = np.load(f'custom_data/train_bins_skirt_10.npy')
-    bins[25] = np.load(f'custom_data/train_bins_skirt.npy')
-    bins[100] = np.load(f'custom_data/train_bins_skirt_100.npy')
-
-    bins = None
-
-
-    nr_of_labels = 6
-    neighbourhood = 50
-    temp_file = '/home/workspace/local-data/temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
-    if not is_skirt:
-        dist = np.load('custom_data/dijkstra.npy')
-    else:
-        dist = np.load('custom_data/dijkstra_skirt.npy')
-
-    visualize_curvature_labels(folder, '02572_1', bins, neighbourhoods=[25, 25, 25], nr_of_labels=[3,6,12])

curvature_label_parallel.py

@@ -4,17 +4,15 @@ import os
 
 if __name__ == '__main__':
 
-    # folder = '/home/workspace/data/dataset/test_skirt_ready_2'
-    # folder = '/home/workspace/data/dataset/local-cache/train_skirt'
-    folder = '/home/workspace/local-data/valid_skirt'
-    ids = os.listdir(folder + '/mesh_posed')
+    folder = '/home/workspace/local-data/cape/train/male'
+    out_folder = '/home/workspace/local-data/cape_processed/train/male'
+    ids = os.listdir(folder)
 
     count = 0
-    step = 10000
+    step = 1
 
-    while count < 20*step:
-    # while count < 10000:
+    while count < len(ids):
         # os.system('nohup python MeshUtils/curvature_labels.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'curv_train_' + str(count) + '_' + str(count+step) + ' > curv_train_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
-        os.system('nohup python curvature_labels.py -f ' + folder + ' -s ' + str(0) + ' -e ' + str(0 + step) + ' -n ' + 'speed_test_' + str(count) + '_' + str(count+step) + ' > nohup/speed/speed_test_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
+        os.system('nohup python curvature_labels_cape.py -if ' + folder + ' -of ' + out_folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'curv_gen_2_cape_train_male_' + str(count) + '_' + str(count+step) + ' > nohup/curv_gen_2_cape_train_male_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('python Data/convert_to_uv.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + '')
         count += step

curvature_labels.py

@@ -4,7 +4,6 @@ import tqdm
 import argparse
 import random
 import string
-import time
 import numpy as np
 import trimesh as tm
 import cmapy as cm
@@ -41,8 +40,6 @@ def export_to_ply(mesh, file):
     output_file.close()
 
 
-
-
 def get_closest_n_points(verts, idx, n):
     dist = sp.spatial.distance.cdist(verts[idx, :], verts)
     dist[dist == 0] = 1e9
@@ -83,65 +80,67 @@ def k_neighbours(k):
     pass
 
 
-def calculate_dist(folder, neighbourhood, frames):
-    out = dict()
-    # frames = frames[int(args.start):int(args.end)]
+def generate_curvatures(folder, neighbourhood):
 
-    for frame in tqdm.tqdm(frames):
-        id = frame.split('_')[0]
-        if id in out.keys():
+    ids = os.listdir(folder)[int(args.start):int(args.end)]
+
+    curvatures_all = []
+
+    for id in tqdm.tqdm(ids):
+        garment_dir = os.path.join(folder, id)
+
+        if not os.path.isdir(garment_dir):
             continue
 
-        mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
-        k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
-        out[id] = k_neighbors
+        # if os.path.exists(garment_dir + '/curvature_25_raw.npy'):
+        #     continue
 
-    return out
+        curvatures_garment = []
 
-def generate_curvatures(folder, neighbourhood, frames):
-
-    # ids = os.listdir(folder)[int(args.start):int(args.end)]
-    # frames = frames[int(args.start):int(args.end)]
-    os.makedirs(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}'), exist_ok=True)
-
-    k_neighbors_cache = calculate_dist(folder, neighbourhood, frames)
-
-    # for frame in tqdm.tqdm(frames):
-
-    #     id = frame.split('_')[0]
-    #     frame_nr = frame.split('_')[1].split('.')[0]
+        garments = [f.replace('.pc16','') for f in os.listdir(garment_dir) if f.find('_') == -1 and f != 'info.mat' and not f.endswith('.obj')]
+        # assert len (garments) == 1
+        if len (garments) != 1:
+            continue
+        garment = garments[0]
     
-    #     # bodies = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
-    #     # offsetss = readPC2(os.path.join(folder, id, garment + '_registered.pc16'), True)['V']
-    #     mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
-    #     _, faces = _maskFaces(np.zeros((14475,3)), F, mask) #np.load(os.path.join(folder, 'faces', f'{id}.npy'))
-    #     v = np.load(os.path.join(folder, 'mesh_posed', f'{id}_{frame_nr}.npz'))['mesh_posed'][mask,:]
+        bodies = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
+        offsetss = readPC2(os.path.join(folder, id, garment + '_registered.pc16'), True)['V']
+        mask = np.squeeze(sio.loadmat(os.path.join(garment_dir, garment + '_mask.mat'))['mask'].astype(bool))
         
-    #     m = tm.Trimesh(v, faces, process=False, validate=False)
+        _, faces = _maskFaces(bodies[0, :, :], F, mask)
+        v = bodies[0, mask, :] + offsetss[0, mask, :]
+        m = tm.Trimesh(v, faces, process=False, validate=False)
 
-    #     outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
-    #     outliers = np.nonzero(outliers)[0]
-    #     outliers_n_neighbours = get_closest_n_points(v, outliers, 2)
-    #     outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
-    #     faces = np.concatenate((faces, outliers_faces), axis=0)
+        outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
+        outliers = np.nonzero(outliers)[0]
+        outliers_n_neighbours = get_closest_n_points(v, outliers, 2)
+        outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
+        faces = np.concatenate((faces, outliers_faces), axis=0)
 
-    #     verts = v
+        step = 0
+        for body, offsets in zip(bodies, offsetss):
+            verts = body + offsets
+            verts = verts[mask, :]
 
-    #     m = tm.Trimesh(verts, faces, process=False, validate=False)
-    #     export_to_ply(m, temp_file)
+            m = tm.Trimesh(verts, faces, process=False, validate=False)
+            export_to_ply(m, temp_file)
 
-    #     cloud = PyntCloud.from_file(temp_file)
+            cloud = PyntCloud.from_file(temp_file)
+            # cloud.plot(mesh=True)
 
-    #     k_neighbors = k_neighbors_cache[id] # np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
-    #     ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
-    #     cloud.add_scalar_field("curvature", ev=ev)
-    #     curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
-    #     out = np.zeros((14475), dtype=np.float32)
-    #     out[mask] = curvature
-        
-    #     np.savez_compressed(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}', f'{id}_{frame_nr}.npz'), mesh_curvature_raw=out)
+            k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :neighbourhood] #cloud.get_neighbors(k=neighbourhood)
+            ev = cloud.add_scalar_field("eigen_values", k_neighbors=k_neighbors)
+            cloud.add_scalar_field("curvature", ev=ev)
+            curvature = np.array(cloud.points["curvature(" + str(neighbourhood+1) + ")"])
+            curvatures_garment.append(curvature)
 
-    return None
+            print(step)
+            step += 1
+
+        # curvatures_all.append(np.array(curvatures_garment))
+        np.save(garment_dir + '/curvature_25_raw.npy', np.array(curvatures_garment))
+
+    return curvatures_all
 
 
 def get_bins(values, nr_of_bins):
@@ -153,23 +152,41 @@ def get_bins(values, nr_of_bins):
     return bins
 
 
-def generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood):
+def generate_curvature_labels(folder, bins, curvatures):
 
-    os.makedirs(os.path.join(folder, f'mesh_curvature_{neighbourhood}_2'), exist_ok=True)
+    ids = os.listdir(folder)#[:10]
     labels = np.linspace(0, bins.shape[0] - 2, bins.shape[0] - 1)
 
-    for i, frame in enumerate(tqdm.tqdm(frames)):
-        # id = frame.split('_')[0]
-        # mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy'))
-        # print(i) 
+    i = 0
+    for id in tqdm.tqdm(ids):
+        garment_dir = os.path.join(folder, id)
+
+        if not os.path.isdir(garment_dir):
+            continue
+
+        garments = [f.replace('.pc16','') for f in os.listdir(garment_dir) if f.find('_') == -1 and f != 'info.mat' and not f.endswith('.obj')]
+        # assert len (garments) == 1
+        if len (garments) != 1:
+            continue
+        garment = garments[0]
+        mask = np.squeeze(sio.loadmat(os.path.join(garment_dir, garment + '_mask.mat'))['mask'].astype(bool))
+
         curvature = curvatures[i]
+        i += 1
 
-        frame_labels = np.zeros((curvature.shape[0]), dtype=np.float32)
-        for j in range(bins.shape[0] - 2):
-            frame_labels[(curvature >= bins[j]) * (curvature < bins[j+1])] = labels[j]
-        frame_labels[curvature >= bins[-2]] = labels[-1]
+        curvature_label = np.zeros((curvature.shape[0], curvature.shape[1]))
+        frame = 0
+        for frame in range(curvature.shape[0]):
+            frame_labels = np.zeros((curvature.shape[1]), dtype=np.float32)
+            for j in range(bins.shape[0] - 2):
+                frame_labels[(curvature[frame, :] >= bins[j]) * (curvature[frame, :] < bins[j+1])] = labels[j]
+            frame_labels[curvature[frame, :] >= bins[-2]] = labels[-1]
+            curvature_label[frame, :] = frame_labels
+            frame += 1
 
-        np.savez_compressed(os.path.join(folder, f'mesh_curvature_{neighbourhood}_2', f'{frame}.npz'), mesh_curvature=frame_labels)
+        unmasked_label = np.zeros((curvature.shape[0], 14475), dtype=np.float32)
+        unmasked_label[:, mask] = curvature_label
+        np.save(os.path.join(garment_dir, "curvature_label_" + str(nr_of_labels) + "_" + str(neighbourhood) + ".npy"), unmasked_label)
         # writePC2(os.path.join(garment_dir, "curvature_label_" + str(nr_of_labels) + "_" + str(neighbourhood) + ".pc16"), unmasked_label, True)
 
 
@@ -240,10 +257,24 @@ def visualize_curvature_labels(folder):
         print()
 
 
-def read_raw_curvatures(folder, frames, neighbourhood):
+def read_raw_curvatures(folder):
+
+    ids = os.listdir(folder)[int(args.start):int(args.end)]
+
     curvatures_all = []
-    for frame in tqdm.tqdm(frames):
-        curv = np.load(os.path.join(folder, f'mesh_curvature_raw_{neighbourhood}', f'{frame}.npz'))['mesh_curvature_raw']
+
+    for id in tqdm.tqdm(ids):
+        garment_dir = os.path.join(folder, id)
+
+        if not os.path.isdir(garment_dir):
+            continue
+
+        garments = [f.replace('.pc16','') for f in os.listdir(garment_dir) if f.find('_') == -1 and f != 'info.mat' and not f.endswith('.obj')]
+        # assert len (garments) == 1
+        if len (garments) != 1:
+            continue
+
+        curv = np.load(garment_dir + '/curvature_25_raw.npy')
         curvatures_all.append(curv)
     
     return curvatures_all
@@ -251,46 +282,28 @@ def read_raw_curvatures(folder, frames, neighbourhood):
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument("-f", "--folder", help = "process the given folder", default=r'/home/workspace/data/dataset/local-cache/train_skirt')
+    parser.add_argument("-f", "--folder", help = "process the given folder", default=r'/home/workspace/data/dataset/test')
     parser.add_argument("-s", "--start", help = "start of range", default=0)
-    parser.add_argument("-e", "--end", help = "end of range", default=30000)
+    parser.add_argument("-e", "--end", help = "end of range", default=1500)
     parser.add_argument("-n", "--name", help = "end of range", default="lhunor")
     args = parser.parse_args()
 
-    is_skirt = True
-    skirt_modifier = '_skirt' if is_skirt else ''
-    F = np.load("custom_utils/smpl_faces" + skirt_modifier + ".npy").astype(np.int32)
+    F = np.load("FeatureLines/superSMPL/smpl_faces.npy")
     # k_neighbours(k=25)
 
     folder = args.folder
-    frames_file = 'frames' + skirt_modifier + '_10p.txt'
-
     nr_of_labels = 6
-    neighbourhood = 100
-    temp_file = '/home/workspace/local-data/temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
-    if not is_skirt:
-        dist = np.load('custom_data/dijkstra.npy')
-    else:
-        dist = np.load('custom_data/dijkstra_skirt.npy')
+    neighbourhood = 25
+    temp_file = 'temp/' + ''.join(random.choice(string.ascii_lowercase) for i in range(10)) + '.ply'
+    dist = np.load('MeshUtils/dijsktra.npy')
+    # dist = np.load('MeshUtils/dijkstra_skirt.npy')
 
-    print('SKIRT' if is_skirt else 'NOT SKIRT')
-    print(f'params: -f {args.folder} -s {args.start} -e {args.end} -n {args.name} -skirt {is_skirt} -neigh {neighbourhood}')
-
-    frames = sorted(os.listdir(os.path.join(folder, 'mesh_posed')))[int(args.start):int(args.end)]
-    frames = [frame.split('.')[0] for frame in frames]
-
-    # frames = []
-    # with open(os.path.join(folder, frames_file), 'r') as f:
-    #     lines = f.readlines()
-    #     lines = [line.strip() for line in lines]
-    # frames = lines[int(args.start):int(args.end)]
-
-    generate_curvatures(folder, neighbourhood, frames)
-    # curvatures = read_raw_curvatures(folder, frames, neighbourhood)
+    generate_curvatures(folder, neighbourhood)
+    # curvatures = read_raw_curvatures(folder)
     # bins = get_bins(curvatures, nr_of_labels)
-    # np.save(f'custom_data/train_bins_skirt_{neighbourhood}.npy', bins)
-    # bins = np.load(f'custom_data/train_bins_skirt_{neighbourhood}.npy')
-    # generate_curvature_labels(folder, bins, curvatures, frames, neighbourhood)
+    # np.save('train_bins_skirt.npy', bins)
+    # bins = np.load('train_bins.npy')
+    # generate_curvature_labels(folder, bins, curvatures)
     # visualize_curvature_labels(folder)
 
     pass

cvae.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_all_post import config
+from configs.config_abl_all_10p_curv_100_r2 import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -41,11 +41,11 @@ from losses import collision_loss as collision_penalty
 from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
-from models.cvae_model import CVAENew as CVAE
+from models.cvae_model import CVAE
 
 
 def init_wandb():
-    run = wandb.init(project='ablation', name=config['name'], notes=config['notes'], dir='/wandb', tags=config['tags'])
+    run = wandb.init(project='ablation', name=config['name'], notes=config['notes'], dir='/wandb')
     artifact = wandb.Artifact('code', type='code')
     artifact.add_file('cvae.py')
     artifact.add_file('models/cvae_model.py')
@@ -58,9 +58,7 @@ def init_wandb():
     artifact.add_file('convnext/models/convnext_tf.py')
     config_file_names = [key for key in sys.modules.keys()  if key.startswith('configs.')]
     for config_file_name in config_file_names:
-        file_name = '/'.join(config_file_name.split('.')) + '.py'
-        if os.path.exists(file_name):
-            artifact.add_file(file_name)
+        artifact.add_file('/'.join(config_file_name.split('.')) + '.py')
     run.log_artifact(artifact)
 
 uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_adaptive=False, is_smpl_orig=config['is_cape'])
@@ -75,7 +73,7 @@ def kl_loss(mean, logvar):
 
 # 3d loss on posed !!!!!!!!!!!!
 
-# @tf.function
+@tf.function
 def train_step(batch):
 
     with tf.GradientTape() as tape:
@@ -478,7 +476,7 @@ def test():
 
 
 if __name__ == '__main__':
-    # init_wandb()
+    init_wandb()
     parser = argparse.ArgumentParser(description='')
     parser.add_argument('--name', type=str)
     args = vars(parser.parse_args())
@@ -496,7 +494,8 @@ if __name__ == '__main__':
                                         use_one_hot=config['use_one_hot'],
                                         use_no_orient=config['remove_global_orientation'],
                                         frames_file=config['frames_file'] if 'frames_file' in config.keys() else None, 
-                                        is_cape=config['is_cape'])
+                                        is_cape=config['is_cape'],
+                                        curv_neighbourhood=config['curv_neighbourhood'])
     static_dataset_train = StaticDataset(folder=config['train_dir'],
                                          signature=config['signature_static'], 
                                          resolutions=[config['resolution']], 
@@ -511,7 +510,8 @@ if __name__ == '__main__':
                                         use_one_hot=config['use_one_hot'],
                                         use_no_orient=config['remove_global_orientation'], 
                                         frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None, 
-                                        is_cape=config['is_cape'])
+                                        is_cape=config['is_cape'],
+                                        curv_neighbourhood=config['curv_neighbourhood'])
     static_dataset_valid = StaticDataset(folder=config['valid_dir'],
                                          signature=config['signature_static'], 
                                          resolutions=[config['resolution']], 

dataset.py

@@ -194,7 +194,8 @@ class DatasetFactory():
                     use_one_hot=False,
                     use_no_orient=True,
                     testing=False,
-                    frames_file=None):
+                    frames_file=None,
+                    curv_neighbourhood=25):
         assert set(resolutions).issubset(set([32, 64, 128, 256, 512]))
 
         dataset_idx = DatasetIdx(folder, shuffle, is_cape=is_cape, testing=testing, frames_file=frames_file)
@@ -260,7 +261,7 @@ class DatasetFactory():
 
 
             if 'mesh_curvature' in signature.keys():
-                mesh_curvature = np.load(os.path.join(folder, 'mesh_curvature', id + separator + frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature']
+                mesh_curvature = np.load(os.path.join(folder, f'mesh_curvature_{curv_neighbourhood}_2' if curv_neighbourhood != 25 else 'mesh_curvature', id + separator + frame_nr + '.npz')); mesh_curvature = mesh_curvature['mesh_curvature']
                 item['mesh_curvature'] = mesh_curvature.astype(np.int32)
 
 
@@ -270,7 +271,8 @@ class DatasetFactory():
                     res_string = '_' + str(res)
 
                 if 'uv_curvature' + res_string in signature.keys():
-                    uv_curvature = np.load(os.path.join(folder, 'uv_curvature' + res_string, id + separator + frame_nr + '.npz')); 
+                    # print(os.path.join(folder, (f'uv_curvature_{curv_neighbourhood}_2' if curv_neighbourhood != 25 else 'uv_curvature')+ res_string, id + separator + frame_nr + '.npz'))
+                    uv_curvature = np.load(os.path.join(folder, (f'uv_curvature_{curv_neighbourhood}_2' if curv_neighbourhood != 25 else 'uv_curvature')+ res_string, id + separator + frame_nr + '.npz')); 
                     if res_string != '':
                         uv_curvature = uv_curvature['uv_curvature'] 
                     else:
@@ -381,15 +383,16 @@ if __name__ == '__main__':
                         #  'uv_shaped_body': (3, 512, 256,),
                           }
 
-    dataset = factory.get_dataset(folder='/home/workspace/local-data/train_skirt',
+    dataset = factory.get_dataset(folder='/home/workspace/data/dataset/local-cache/train_skirt',
                                   batch_size=1,
                                   signature=signature_dynamic,
                                   resolutions=[512],
                                   do_repeat=False,
                                   use_one_hot=False,
                                   is_cape=False,
-                                  frames_file='frames_dress_30k.txt')
-    static_dataset = StaticDataset(folder='/home/workspace/local-data/train_skirt',
+                                  frames_file='frames_skirt_10p.txt',
+                                  curv_neighbourhood=25)
+    static_dataset = StaticDataset(folder='/home/workspace/data/dataset/local-cache/train_skirt',
                                    signature=signature_static,
                                    resolutions=[512],
                                   is_cape=False)

evaluate_ae.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from configs.config_abl_all_post import config
+from configs.config_abl_all_10p_curv_25_r2 import config
 
 os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
 import tensorflow as tf
@@ -42,7 +42,7 @@ from losses import collision_loss as collision_penalty
 from custom_utils.custom_utils import batch_to_dict, load_model, plot_3d, UV2Mesh, get_summary_html, maskFacesBatch, _maskFaces, plot_3d_collision
 from dataset import DatasetFactory, StaticDataset
 from custom_utils.smpl import SMPL
-from models.cvae_model import CVAENew as CVAE
+from models.cvae_model import CVAE
 from custom_utils.supersmpl_io import writeOBJ
 # from unif_metric import chamfer_and_score
 
@@ -102,7 +102,7 @@ if __name__ == '__main__':
     ids = [id.split('_')[0] for id in ids]
     ids = set(ids)
 
-    iteration = ''
+    iteration = '70000'
 
     load_model(model, 'checkpoints/post/' + checkpoint_name, iteration=iteration)
     # load_model(model, 'checkpoints/' + 'abl_all_120k')

fix_curv_uv_loc.py

@@ -1,23 +0,0 @@
-import os
-import shutil
-import tqdm
-
-if __name__ == '__main__':
-    folder = '/home/workspace/data/dataset/local-cache/train_skirt'
-    is_skirt = True 
-    skirt_modifier = '_skirt' if is_skirt else ''
-    frames_file = 'frames' + skirt_modifier + '_10p.txt'
-    neighbourhood = 10
-
-    frames = []
-    with open(os.path.join(folder, frames_file), 'r') as f:
-        lines = f.readlines()
-        lines = [line.strip() for line in lines]
-    frames = lines
-
-    os.makedirs(os.path.join(folder, f'uv_curvature_{neighbourhood}'), exist_ok=True)
-    for frame in tqdm.tqdm(frames):
-        id = frame.split('_')[0]
-        src = os.path.join(folder, f'{id}', 'curvature_uv_10', f'{frame}.npz')
-        dst = os.path.join(folder, f'uv_curvature_{neighbourhood}', f'{frame}.npz')
-        shutil.move(src, dst)

run_parallel.py

@@ -4,26 +4,24 @@ import os
 
 if __name__ == '__main__':
 
-    # folder = '/home/workspace/data/dataset/local-cache/train_skirt'
     folder = '/home/workspace/data/dataset/test_skirt_ready_2'
-    # folder = '/home/workspace/local-data/valid_skirt'
     # folder = '/home/workspace/local-data/cape_processed/train/male'
     # folder = '/home/workspace/local-data/valid_skirt'
     # folder = '/home/workspace/local-data/valid'
     # ids = os.listdir(folder)
-    # ids = os.listdir(folder + '/mesh_posed_no_orient')#[280000:]
+    ids = os.listdir(folder + '/mesh_posed_no_orient')#[280000:]
     skirt = 0
 
     count = 0
-    step = 20
+    step = 2000
 
     # !!!!!!! change skirt parameter in script
 
-    while count < 150:
+    while count < len(ids):
         # os.system('nohup python convert_to_uv_cape.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_2_cape_train_male_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_2_cape_train_male_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('nohup python convert_to_uv.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_unposed_onehot_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_train_unposed_onehot_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('nohup python convert_to_uv_local.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_local_unposed_onehot_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_train_local_unposed_onehot_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
-        os.system('nohup python convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_curv_2_10_test_skirt_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_curv_2_10_test_skirt_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
+        os.system('nohup python convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_posed_no_orient_test_skirt_ready_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_posed_no_orient_test_skirt_ready_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('nohup python convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_train_skirt_local_unposed_onehot_3_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_train_skirt_local_unposed_onehot_3_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('nohup python convert_to_uv_local_single.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'to_uv_valid_skirt_local_unposed_onehot_3_' + str(count) + '_' + str(count+step) + ' > nohup/to_uv_valid_skirt_local_unposed_onehot_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')
         # os.system('nohup python downscale.py -f ' + folder + ' -s ' + str(count) + ' -e ' + str(count + step) + ' -n ' + 'downscale_valid_posed_no_orient_' + str(count) + '_' + str(count+step) + ' > nohup/downscale_valid_posed_no_orient_' + str(count) + '_' + str(count+step) + '.out' + ' 2>&1 &')